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from fastapi import FastAPI, HTTPException |
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from typing import Any |
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from pydantic import BaseModel |
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from os import getenv |
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from huggingface_hub import InferenceClient |
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import random |
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from json_repair import repair_json |
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import nltk |
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import sys |
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app = FastAPI() |
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nltk.download('punkt') |
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tokenizer = nltk.data.load('tokenizers/punkt/english.pickle') |
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HF_TOKEN = getenv("HF_TOKEN") |
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MODEL = "mistralai/Mistral-7B-Instruct-v0.2" |
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FALLBACK_MODELS = [ |
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"mistralai/Mixtral-8x7B-Instruct-v0.1", |
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"mistralai/Mistral-7B-Instruct-v0.2", "mistralai/Mistral-7B-Instruct-v0.1" |
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] |
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class InputData(BaseModel): |
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model: str |
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system_prompt_template: str |
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prompt_template: str |
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system_prompt: str |
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user_input: str |
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json_prompt: str |
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history: str = "" |
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@app.post("/generate-response/") |
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async def generate_response(data: InputData) -> Any: |
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client = InferenceClient(model=data.model, token=HF_TOKEN) |
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sentences = tokenizer.tokenize(data.user_input) |
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data_dict = {'###New response###': [], '###Sentence count###': 0} |
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for i, sentence in enumerate(sentences): |
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data_dict["###New response###"].append(sentence) |
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data_dict["###Sentence count###"] = i + 1 |
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data.history += data.prompt_template.replace("{Prompt}", str(data_dict)) |
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inputs = ( |
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data.system_prompt_template.replace("{SystemPrompt}", |
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data.system_prompt) + |
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data.system_prompt_template.replace("{SystemPrompt}", data.json_prompt) + |
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data.history) |
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seed = random.randint(0, 2**32 - 1) |
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try: |
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response = client.text_generation(inputs, |
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temperature=1.0, |
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max_new_tokens=1000, |
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seed=seed) |
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strict_response = str(response) |
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repaired_response = repair_json(strict_response, |
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return_objects=True) |
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if isinstance(repaired_response, str): |
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raise HTTPException(status_code=500, detail="Invalid response from model") |
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else: |
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cleaned_response = {} |
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for key, value in repaired_response.items(): |
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cleaned_key = key.replace("###", "") |
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cleaned_response[cleaned_key] = value |
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for i, text in enumerate(cleaned_response["New response"]): |
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if i <= 2: |
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sentences = tokenizer.tokenize(text) |
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if sentences: |
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cleaned_response["New response"][i] = sentences[0] |
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else: |
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del cleaned_response["New response"][i] |
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if cleaned_response.get("Sentence count"): |
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if cleaned_response["Sentence count"] > 3: |
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cleaned_response["Sentence count"] = 3 |
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else: |
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cleaned_response["Sentence count"] = len(cleaned_response["New response"]) |
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data.history += str(cleaned_response) |
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return cleaned_response |
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except Exception as e: |
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print(f"Model {data.model} failed with error: {e}") |
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raise HTTPException(status_code=500, detail=f"Model {data.model} failed to generate response") |
